The Development of Floral Scent Research: A Comprehensive Bibliometric Analysis (1987–2022)
Bibliographic record
Abstract
Floral scent (FS) plays a pivotal role in maintaining ecological equilibrium within plant populations and ecosystems while also bearing significance for human well-being. Despite the growing interest in FS research, there exists a dearth of comprehensive analyses on research trends, contemporary topics, and their broader implications. In this study, we employ bibliometric techniques using data from the Web of Science Core Collection spanning 1987-2022 to offer a quantitative overview of the scientific literature surrounding FS by examining the annual publication outputs, popular research areas, temporal trends in keywords, geographic distribution of relevant studies, institutions, co-organizations, as well as relevant authors. Our findings reveal a marked upsurge in FS publications, notably within the domains of Food Science Technology, Plant Sciences, Chemistry, Agriculture, Biochemistry, and Molecular Biology. The research landscape in FS primarily encompasses evolutionary dynamics, volatile compound analyses, biosynthesis mechanisms, and essential oil properties. These research trends signify a transition from micro-level exploration, focusing on individual pollination ecological functions of FS, to a macro-perspective that emphasizes FS's overarching impact on species diversity and ecosystem stability. This shift extends from the investigation of singular sensory attributes of FS to a holistic evaluation of their role in food production, quality, and yield enhancement. It encompasses a move away from mere FS extraction towards the examination of antioxidant potential within phenolic compounds and other industrial applications. Thus, improving research methodologies, strengthening interdisciplinary collaboration on an international scale, and delving deeper into the multifaceted ecological functions of floral diversity and their societal implications will be paramount.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.076 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".